Trading strategy on Bitcoin, MATIC and BNB: using ADX to identify significant trends

A previous article examined a strategy on Ethereum (ETH) based on the calculation of ADX, an indicator whose acronym stands for “Average Directional Movement Index” and is used to measure the strength of a trend. 

If the indicator tends toward low values then the trend will be almost absent, if the ADX takes high values then the underlying trend will be more significant.

Backtest of the ADX trading system on Bitcoin, MATIC and BNB

Since this trading strategy was providing interesting results on ETH, the decision was made to test the same logic also on other cryptocurrencies. Specifically, the strategy involved long-only entries, with ADX less than 50 on the highest highs of the last 200 bars (at 15 minutes). 

Exits were on the lowest lows of the last 200 bars, as well as possibly at stop loss (5% of the position’s value), or after a maximum of 5 days in the market.

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Figure 1. Example Entry and Exit Pattern on MATIC

How might this logic work on other cryptocurrencies? 

Testing the trading system on Bitcoin (BTC), MATIC, and BNB shows from the outset some continuity from the results obtained on ETH. 

We chose to use ADX values below 45 (slightly more stringent than the ETH strategy) for these crypto assets because it provides the best results (Figures 2-3-4) in overall terms. The profit goes down as the ADX values go down, but the average trade goes up (except on BNB on which other ADX values seem to work), consequently the drawdown goes down as the ADX filter becomes more stringent. 

This is certainly an indication of the good filtering job done by the indicator, as the quality of the average trade has increased.

The position is always fixed and is equivalent to $10,000 in monetary value. The average trades that are obtained are undoubtedly capacious enough to trade in the real market.

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Figure 2. BTC trading system optimization based on ADX

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Figure 3. MATIC trading system optimization based on ADX

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Figure 4. BNB trading system optimization based on ADX

In the next figures (5-6-7), it can be seen that the individual curves are also appreciable. ADX seems to be able to provide interesting results even in younger and newer markets such as cryptocurrencies.

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Figure 5. Equity Line trading system BTC based on the ADX

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Figure 6. Equity Line trading system MATIC based on the ADX
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Figure 7. Equity Line trading system BNB based on the ADX

Also interesting to see is the comparison between the simple buy&hold of these products and the strategy just used. 

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Figure 8. Buy&Hold on BTC

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Figure 9. Buy&Hold on MATIC

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Figure 10. Buy&Hold on BNB

Conclusions on the ADX trading system on Bitcoin, MATIC, and BNB

What immediately jumps out is that in the most recent part of the backtest, or more generally in the last year and a half, the strategy has performed better than the simple buy&hold of the instrument. 

In fact, while the overall profit of the strategy is lower than that of the buy&hold, there are no sharp drawdowns like those recorded by the markets between the beginning of 2022 and the end of the same year. 

The automated strategy, with the help of ADX, was able to skim those market phases where it was not convenient to enter and take long positions.

Even on Bitcoin – a market that is certainly more prone to the development of automatic strategies since it is the oldest among crypto assets – the equity peaks of this strategy have recently been reached, in the face of the conspicuous rebound that the same market made in the early months of 2023.

In this case as well, the automated strategy was able on the one hand to contain risk and on the other hand to update historical highs more frequently than the classic buy-and-hold approach.

Until next time!

Andrea Unger

ADX: trading system for measuring the strength of a trend on Ethereum

This new article will examine, and test on Ethereum, one of the most widely used indicators in trading for trend following type strategies: ADX. 

This indicator, whose acronym stands for “Average Directional Movement Index,” is used to measure the strength of a trend. 

If the indicator tends toward low values then the trend will be almost absent, but if the ADX takes high values then the underlying trend will be more significant.

Trading: the study of the ADX on Ethereum

An example of how ADX is used on an Ethereum chart can be seen in Figure 1. The indicator ranging from 0 to 100 is calculated over the last 5 days, and as can be seen in the initial part of the chart, in the presence of extended and explicit trends, the indicator takes values above the average threshold line of 50. 

During the up-trend that occurred on Ethereum in early 2023 it was seen how the ADX went from a value of 20 to highs of over 90.

The crypto market does spend a lot of time in a trend, whether bullish or bearish, and it is therefore very likely to see very high ADX values as in the case just described.

An important distinction is also made by the fact that ADX tends to rise in both up-trend and down-trend situations. In sideways market situations, or where the trend is absent, the ADX will be at lower values, or at least generally contained between 0 and 50.

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figure1. Graphical representation of the ADX on Ethereum

Having made these due clarifications, the question remains as to how the information retrieved from the indicator can be used in a trading strategy. What would seem most logical, simply by looking at the chart, would be to avoid trend-following trades in very extended trend phases with the ADX on very high values. 

This is because when the market movement is already quite extensive and the ADX is marking high levels, continuing trend-following may not bear the desired fruit

How to use ADX in trading: a test on Ethereum

We therefore proceed to build a strategy (entry example visible in Figure 2) that makes only long trades on the Ethereum spot market. 

The time frame used is 15 minutes, and the highest high of the last 200 bars will be used as the entry level, while the lowest low of the last 200 bars will be used as a kind of trailing stop for long trades. 

This trigger (or entry level) is calculated through what is generally called the Price Channel (or also Donchian Channel) a very useful indicator for setting up trend-following strategies.

For exits, on the other hand, the strategy provides a stop loss, that is, a level on which to set the maximum loss, equal to 5% of the value of the position taken ($10,000), and that is $500.

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Figure 2. Example of the entry of the ADX strategy on Ethereum ADX su Ethereum

Figure 3 shows the results of optimizing the ADX values applied to the strategy just described. The results range from 5 to 100, and the condition included in the code requires that we operate only when ADX is below a certain threshold. In fact, as the ADX value decreases, the condition becomes more and more stringent, and this is evidenced by the number of trades, which decreases as the ADX values decrease.

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Figure 3. ADX optimization on Ethereum.

What emerges from these results is that already at the base, the strategy makes excellent trades. In fact, with ADX values below 100,95,90 and generally high (and therefore low) values, the strategy achieves excellent results. As one moves toward more significant ADX values, one notices that the average trade, or average profit per trade, increases and the relative drawdown decreases. Between 30 and 60 one could isolate the most interesting cases, but the point that provides the most certainty is 50.

By setting this value, the strategy takes on a very positive profit curve, visible in Figure 4, and also the average trade, as observed in the optimization, reaches $194, about 2% of the value of the position taken. Certainly a very capacious value and potentially able to cover the costs of trading in this market.

Hence a very good result, with 2022 positive as well, which is certainly another point of merit of this strategy compared to a more simple “buy-and-hold” given the strong shocks made by this market in the year in question. Even in the current year, 2023, in the face of a rebound in the underlying, the strategy was also able to tweak and improve on its previous all-time highs. 


In conclusion, it can be said that the quality of trades has thus improved as a result of the addition of the indicator and that ADX is certainly a filter to remember when trend-following.

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Figure 4. Equity line ADX strategy on Ethereum
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Figure 5. Average trade ADX strategy on Ethereum
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Figure 6. Annual results ADX strategy on Ethereum

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Figure 7. Take profit optimization ADX strategy on Ethereum

Ethereum, more so than other cryptocurrencies, has also proven to be a pliant market, capable of reacting well to different types of filters and conditions, and as mentioned, ADX has also been able to demonstrate that it can distinguish trend phases from lateral phases with good success. 

The strategy at this point in development could be further refined, for example by limiting the number of days in the market or inserting other conditions to reduce the final number of trades. Adding a take profit, on the other hand, results as a limitation to the system, which working in a notoriously volatile and explosive market would end up cutting into profits too soon (Figure 7).

Until next time!

Andrea Unger

Intraday Bias Trading System on Ethereum (ETH)

In this new roundup we will look at a strategy called ‘bias’ that analyzes the recurring behaviors of a specific market. The strategy identifies time slots, or days of the week for example, when it is more convenient to buy or sell a specific underlying asset.

Specifically, this type of strategy exploits information from all the recurring behaviors that occur over a set period. Depending on the duration of a trade, three different macro-categories of bias strategies can be distinguished:

  • Intraday
  • Weekly
  • Monthly or ‘Seasonal’

In the specific case of this article, the analysis focuses on a rather fast time horizon, the intraday one, and we are going to test the idea on the second most capitalized cryptocurrency in the world: Ethereum.

Testing the Bias trading strategy on Ethereum (ETH)

We proceed with a test on the Unger Academy® software, the Bias Finder™, which will allow in a very simple way to evaluate the average historical price trend of Ethereum. Figure 1 shows this trend over the period from 2018 to 2021.

It can be seen almost at a glance how in the initial hours of the session, which starts at midnight (GTC), there is a bearish trend that is promptly recovered in the late morning hours, around 11:00 AM, and lasts until the final hours of the session in a sort of harmonious cycle of declines and rises that follows one another day after day.

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Figure 1. Daily Bias on Ethereum (ETH)


Clearly, the trend analyzed is only an average result, so it does not amount to a guarantee that every day of the year Ethereum moves following this trend. However, the truth remains that the sum of the movements recorded on ETH led to these results, so it is worth investigating based on the time frames uncovered by the software.

Specifically, using this information to build an automated strategy, one can see in the following figures how buying daily at 11:00 AM and closing the trade a quarter of an hour before the session closes, at 11:45 PM, immediately yields an excellent profit curve (Figure 2).

Backtesting the strategy

The size used for one trade in this backtest is $1000. The backtest starts in 2017 and ends in the early days of the year 2023.

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Figure 2. Equity Curve trading system Bias Long on Ethereum (ETH)


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Figure 3. Average Trade trading system Bias Long on Ethereum (ETH)

The sore point of this strategy concerns the average trade, which barely touches $3.27 (or 0.327% of the position’s countervalue). Certainly not a figure that allows for safe trading with this system in the real market, because the commission costs and slippage (i.e., the difference between theoretical and actual price) that a trader would have to pay, could be quantified at around $2 (0.2% of the position’s countervalue) hence remaining with a meager 0.127% (3.27-2=1.27$) as the net average trade.

In any case, this result is interesting in that it must be remembered that the system, as designed, stays in the market only a few hours and makes a trade every day of the backtest. In short, a bit like staying in the market 12 hours on and 12 hours off, continuously, every day of the year. It becomes more understandable that it is not possible to aspire to extremely large average trades, but one can always try to improve the value by adding a condition that limits the number of trades in the history and make the strategy more effective and selective.

In the figures below we see how adding a condition, found within a list of proprietary patterns, improves results. Indeed, by isolating the trade on days when today’s session high is at least 0.75% higher than the previous session high, the strategy goes from an average trade of $3.27 to $7.50 (0.75% of the position).

This condition identifies an additional confirmation situation in addition to the hourly signal in which the trend of the current session is bullish, at least compared to the previous day. One will proceed to open the long trade (buy trade) only if by 11:00 AM today, the market is showing strength compared to the day before. A kind of further confirmation on the short-term trend of the market.

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Figure 4. Equity Curve trading system Bias Long on Ethereum (ETH) with Pattern addition


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Figure 5. Average trade trading system Bias Long on Ethereum (ETH) with Pattern addition


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Figure 6. Annual trading system Bias Long trading results on Ethereum (ETH) of the filtered strateg


The profit curve also takes a more graceful shape, with good results in all years of the backtest, including 2022, a year that we know has been very difficult for ETH and all crypto in general. Moreover, 2022 was left out of the initial backtest run by the software. Hence, even the ‘out of sample’ period, the portion of time not considered in the backtest, was more than positive, and this only corroborates the basic idea of the system.

Refine the strategy

However, the strategy could be further refined, or slightly modified, for example by adding a stop-loss (point of maximum loss acceptable by the system), or a take-profit (point of maximum profit beyond which to cash out the gain) or another condition suitable to further limiting the number of trades. This is meant to be just an initial idea to show what are the tangible advantages in operating with an automated strategy compared to a more traditional ‘buy-and-hold’.

On the other hand, it must be acknowledged that 5-6 years of backtesting may not be enough to make a definitive judgment on the strategy, especially since it is biased, which is usually a strategy that may harbor more pitfalls than more classic types of entry such as trend-following. What is certain is that the more than 450 trades obtained during the backtest period represent a reliable statistical sample.

As further counter-evidence on the work done, we now proceed to use this same strategy also on the main cryptocurrency market, Bitcoin, where good results continue to be obtained, in the wake of what we have seen on Ethereum (Figure 7).

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Figure 7. Equity line trading system Bias Long on BitCoin (BTC)

This additional test adds effectiveness to the work done on ETH and gives the strategy even more robustness.

Cryptocurrencies are certainly a young market, still immature, but beginning to mature over time. Strategies away from the more traditional ones, such as precisely bias, are beginning to show comforting signs even on these financial products, an indication that perhaps these markets could be used in a well-diversified portfolio context.

Until next time!

Andrea Unger